[1904.02101] The Landscape of R Packages for Automated Exploratory Data Analysisopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering. There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of twelve popular R packages to identify the parts of analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development.

1 mentions: @y__mattu
Date: 2020/06/27 06:52

Related Entries

Read more [1908.03971] TAPER: Time-Aware Patient EHR Representationopen searchopen navigation menucontact arXi...
0 users, 1 mentions 2020/05/15 11:21
Read more Uploading & Updating Datasets with the Kaggle API
0 users, 1 mentions 2020/05/23 12:52
Read more 自然言語処理ナイト - connpass
0 users, 28 mentions 2020/06/15 12:58
Read more GitHub - alexa/alexa-with-dstc9-track1-dataset: DSTC9 Track 1 - Beyond Domain APIs: Task-oriented Co...
0 users, 1 mentions 2020/06/16 02:21
Read more NLP4MusA
0 users, 1 mentions 2020/06/25 14:21